Evaluation of TRMM multi-satellite precipitation analysis (TMPA) against terrestrial measurement over a humid sub-tropical basin,India

To support the GPM mission which is homologous to its predecessor, the Tropical Rainfall Measuring Mission (TRMM), this study has been undertaken to evaluate the accuracy of Tropical Rainfall Measuring Mission multi-satellite precipitation analysis (TMPA) daily-accumulated precipitation products for 5 years (2008–2012) using the statistical methods and contingency table method. The analysis was performed on daily, monthly, seasonal and yearly basis. The TMPA precipitation estimates were also evaluated for each grid point i.e. 0.25° × 0.25° and for 18 rain gauge stations of the Betwa River basin, India. Results indicated that TMPA precipitation overestimates the daily and monthly precipitation in general, particularly for the middle sub-basin in the non-monsoon season. Furthermore, precision of TMPA precipitation estimates declines with the decrease of altitude at both grid and sub-basin scale. The study also revealed that TMPA precipitation estimates provide better accuracy in the upstream of the basin compared to downstream basin. Nevertheless, the detection capability of daily TMPA precipitation improves with increase in altitude for drizzle rain events. However, the detection capability decreases during non-monsoon and monsoon seasons when capturing moderate and heavy rain events, respectively. The veracity of TMPA precipitation estimates was improved during the rainy season than during the dry season at all scenarios investigated. The analyses suggest that there is a need for better precipitation estimation algorithm and extensive accuracy verification against terrestrial precipitation measurement to capture the different types of rain events more reliably over the sub-humid tropical regions of India.     

Accuracy validation of TRMM Multisatellite Precipitation Analysis daily precipitation products in the Lancang River Basin of China

Using statistical methods and contingency table method, this paper evaluates the accuracy of 12 years (1998–2009) Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) daily-accumulated precipitation products within a year, the dry season, and rain season for each of the five subbasins and for each grid point (0.25?×?0.25°) in the Lancang River basin by comparing the results with data from the 35 rain gauges. The results indicate that TMPA daily precipitation estimates tend to show an underestimation comparing to the rain gauge daily precipitations under any scenarios, especially for the middle stream in the dry season. The accuracy of TMPA-averaged precipitation deteriorates with the increase of elevation at both basin and grid scale, with upstream and downstream having the worst and best accuracy, respectively. A fair capability was shown when using daily TMPA accumulations to detect rain events at drizzle rain and this capability improves with the increase of elevation. However, the capability deteriorates when it is used to detect moderate rain and heavy rain events. The accuracy of TMPA precipitation estimate products is better in the rain season than in the dry season at all scenarios. Time difference and elevation are the main factors that have impact on the accuracy of TMPA daily-accumulated precipitation products.     

Increasing trend of northeast monsoon rainfall over the equatorial Indian Ocean and peninsular India

Peninsular India and Sri Lanka receive major part of their annual rainfall during the northeast monsoon season (October–December). The long-term trend in the northeast monsoon rainfall over the Indian Ocean and peninsular India is examined in the vicinity of global warming scenario using the Global Precipitation Climatology Project (GPCP) dataset available for the period 1979–2010. The result shows a significant increasing trend in rainfall rate of about 0.5 mm day^?1 decade^?1 over a large region bounded by 10 °S–10 °N and 55 °E–100 °E. The interannual variability of seasonal rainfall rate over peninsular India using conventional rain gauge data is also investigated in conjunction to the Indian Ocean dipole. The homogeneous rain gauge data developed by Indian Institute of Tropical Meteorology over peninsular India also exhibit the considerable upward rainfall trend of about 0.4 mm day^?1 decade^?1 during this period. The associated outgoing longwave radiation shows coherent decrease in the order of 2 W?m^?2 decade^?1 over the rainfall increase region.     

Validating a rapid-update satellite precipitation analysis across telescoping space and time scales

In order to properly utilize remotely sensed precipitation estimates in hydrometeorological applications, knowledge of the accuracy of the estimates are needed. However, relatively few ground validation networks operate with the necessary spatial density and time-resolution required for validation of high-resolution precipitation products (HRPP) generated at fine space and time scales (e.g., hourly accumulations produced on a 0.25° spatial scale). In this article, we examine over-land validation statistics for an operationally designed, meteorological satellite-based global rainfall analysis that blends intermittent passive microwave-derived rainfall estimates aboard a variety of low Earth-orbiting satellite platforms with sub-hourly time sampling capabilities of visible and infrared imagers aboard operational geostationary platforms. The validation dataset is comprised of raingauge data collected from the dense, nearly homogeneous, 1-min reporting Automated Weather Station (network of the Korean Meteorological Administration during the June to August 2000 summer monsoon season. The space-time RMS error, mean bias, and correlation matrices were computed using various time windows for the gauge averaging, centered about the satellite observation time. For ±10 min time window, a correlation of 0.6 was achieved at 0.1° spatial scale by averaging more than 3 days; coarsening the spatial scale to 1.8° produced the same correlation by averaging over 1 h. Finer than approximately 24-h and 1° time and space scales, respectively, a rapid decay of the error statistics was obtained by trading-off either spatial or time resolution. Beyond a daily time scale, the blended estimates were nearly unbiased and with an RMS error of no worse than 1 mm day^?1.     

Sensitivity of precipitation to sea surface temperature over the tropical summer monsoon region—and its quantification

Over the tropical oceans, higher sea surface temperatures (SST, above 26 °C) in summer are generally accompanied by increased precipitation. However, it has been argued for the last three decades that, any monotonic increase in precipitation with respect to SST is limited to an upper threshold of 28–29.5 °C, and beyond this, the relationship fails. Based on this assessment it has often been presumed that, since the mean SSTs over the Asian monsoon basins (Indian Ocean and north-west Pacific) are mostly above the threshold, SST does not play an active role on the summer monsoon variability. It also implies that increasing SSTs due to a changing climate need not result in increasing monsoon precipitation. The current study shows that the response of precipitation to SST has a time lag, that too with a spatial variability over the monsoon basins. Taking this lag into account, the results here show that enhanced convection occurs even up to the SST maxima of 31 °C averaged over these basins, challenging any claim of an upper threshold for the SST-convection variability. The study provides us with a novel method to quantify the SST-precipitation relationship. The rate of increase is similar across the basins, with precipitation increasing at ~2 mm day^?1 for an increase of 1 °C in SST. This means that even the high SSTs over the monsoon basins do play an active role on the monsoon variability, challenging previous assumptions. Since the response of precipitation to SST variability is visible in a few days, it would also imply that including realistic ocean–atmosphere coupling is crucial even for short term monsoon weather forecasts. Though recent studies suggest a weakening of the monsoon circulation over the last few decades, results here suggest an increased precipitation over the tropical monsoon regions, in a global warming environment with increased SSTs. Thus the signature of SST is found to be significant for the Asian summer monsoon, in a quantifiable manner, seamlessly through all the timescales—from short-term intraseasonal to long-term climate scales.     

A return to wet conditions over Africa: 1995–2010

Climatic trends over sub-Saharan Africa are described using major river flows, European Community Medium-Range Weather Forecasts, Coupled Forecast System, global land surface data assimilation and National Center for Environmental Prediction reanalysis, Global Precipitation Climate Center gauge data, and satellite observations in the period 1995–2010. The Niger and Zambezi rivers reached flow levels last seen in the 1950s (2,000 and 5,000 m³ s^?1, respectively), and rainfall across the Congo Basin increased steadily ~+0.16 mm day^?1 year^?1. Weather events that contributed to flooding are studied and include the Zambezi tropical trough of 4 January 2008 and the Sahelian easterly wave of 19 July 2010. Diurnal summer rainfall increased threefold over the 1995–2010 period in conjunction with a strengthened land–sea temperature contrast, onshore flow, and afternoon uplift. 700 mb zonal winds over East Africa became easterly after 2001, so clean Indian Ocean air was entrained to the Congo, improving convective efficiency. Relationships between the African monsoon circulation and global teleconnections are explored. Zonal wind convergence around the Congo appears related with the tropical multi-decadal oscillation and signals in the Atlantic during the study period.     

Quantifying the effects of climate trends in the past 43 years (1961–2003) on crop growth and water demand in the North China Plain

This paper explores changes in climatic variables, including solar radiation, rainfall, fraction of diffuse radiation (FDR) and temperature, during wheat season (October to May) and maize season (June to September) from 1961 to 2003 at four sites in the North China Plain (NCP), and then evaluates the effects of these changes on crop growth processes, productivity and water demand by using the Agricultural Production Systems Simulator. A significant decline in radiation and rainfall was detected during the 43 years, while both temperature and FDR exhibit an increasing trend in both wheat and maize seasons. The average trend of each climatic variable for each crop season from the four sites is that radiation decreased by 13.2 and 6.2 MJ m^?2 a^?1, precipitation decreased by 0.1 and 1.8 mm a^?1, minimum temperature increased by 0.05 and 0.02°C a^?1, maximum temperature increased by 0.03 and 0.01°C a^?1, FDR increased by 0.21 and 0.38% a^?1 during wheat and maize season, respectively. Simulated crop water demand and potential yield was significantly decreased because of the declining trend in solar radiation. On average, crop water demand was decreased by 2.3 mm a^?1 for wheat and 1.8 mm a^?1 for maize if changes in crop variety were not considered. Simulated potential crop yields under fully irrigated condition declined about 45.3 kg ha^?1 a^?1 for wheat and 51.4 kg ha^?1 a^?1 for maize at the northern sites, Beijing and Tianjin. They had no significant changes in the southern sites, Jinan and Zhengzhou. Irrigation, fertilization development and crop variety improvement are main factors to contribute to the increase in actual crop yield for the wheat–maize double cropping system, contrasted to the decline in the potential crop yield. Further research on how the improvement in crop varieties and management practices can counteract the impact of climatic change may provide insight into the future sustainability of wheat–maize double crop rotations in the NCP.     

Evaporation estimates from Nasser Lake, Egypt, based on three floating station data and Bowen ratio energy budget

Nasser Lake is located in a hyper-arid region in the south of Egypt. Evaporation is by far the most important factor in explaining the water losses from the lake. To obtain better management scenarios for Nasser Lake, an accurate estimation of the lake evaporation losses thus is essential. This paper presents an update of previous evaporation estimates, making use of local meteorological and hydrological data collected from instrumented platforms (floating weather stations) at three locations on the lake: at Raft, Allaqi, and Abusembel (respectively at 2, 75, and 280 km upstream of the Aswan High Dam). Results from six conventional evaporation quantification methods were compared with the values obtained by the Bowen ratio energy budget method (BREB). The results of the BREB method showed that there is no significant difference between the evaporation rates at Allaqi and Abusembel. At Raft, higher evaporation rates were obtained, which were assumed to be overestimated due to the high uncertainty of the Bowen ratio (BR) parameter. The average BR value at Allaqi and Abusembel was used to eliminate this overestimates evaporation. Variance-based sensitivity and uncertainty analyses on the BREB results were conducted based on quasi-Monte Carlo sequences (Latin Hypercube sampling). The standard deviation of the total uncertainty on the BREB evaporation rate was found to be 0.62 mm day^?1. The parameter controlling the change in stored energy, followed by the BR parameter, was found to be the most sensitive parameters. Several of the six conventional methods showed substantial bias when compared with the BREB method. These were modified to eliminate the bias. When compared to the BREB-based values, the Penman method showed most favorably for the daily time scale, while for the monthly scale, the Priestley–Taylor and the deBruin–Keijman methods showed best agreement. Differences in mean evaporation estimates of these methods (against the BREB method) were found to be in the range 0.14 and 0.36 mm day^?1. All estimates were based calculations at the daily time scale covering a 10-year period (1995–2004).     

Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran

This paper examines the potential for the use of artificial neural networks (ANNs) to estimate the reference crop evapotranspiration (ET₀) based on air temperature data under humid subtropical conditions on the southern coast of the Caspian Sea situated in the north of Iran. The input variables for the networks were the maximum and minimum air temperature and extraterrestrial radiation. The temperature data were obtained from eight meteorological stations with a range of latitude, longitude, and elevation throughout the study area. A comparison of the estimates provided by the ANNs and by Hargreaves equation was also conducted. The FAO-56 Penman–Monteith model was used as a reference model for assessing the performance of the two approaches. The results of this study showed that ANNs using air temperature data successfully estimated the daily ET₀ and that the ANNs with an R ² of 0.95 and a root mean square error (RMSE) of 0.41 mm day^?1 simulated ET₀ better than the Hargreaves equation, which had an R ² of 0.91 and a RMSE of 0.51 mm day^?1.     

Palaeoprecipitation reconstruction by inverse modelling using the isotopic signal of loess organic matter: application to the Nußloch loess sequence (Rhine Valley, Germany)

A modified version of the Biome4 vegetation model for simulation of the mean δ¹³C of plant communities is presented, and used to reconstruct palaeoprecipitation. We treat all fractionations by C3 and C4 plants in all coexistent Plant Functional Types, weighted by their respective net primary production. We constrain the range of variation in the intracellular versus atmospheric CO₂ concentration by fixing a lower limit. Finally, we replace some constant parameters by functions of external forcing to account for their responses to environmental variation. The new version of Biome4 was applied as an inverse model and tested on three modern data sets. The fit between observations and simulations is very close to the 1:1 relationship, with respective slopes of 0.90±0.02 (r ²=0.98, n=29) for δ¹³C and 0.97±0.06 (r ²=0.90, n=29) for precipitation. Inverse modelling was applied using the Metropolis-Hastings algorithm to the Nußloch loess sequence. Over the last glaciation, simulated palaeoprecipitation varies between 240 mm year^?1 and 400 mm year^?1. This study clearly demonstrates atmospheric teleconnections with the Greenland ice-sheet extension, by matching Dansgaard-Oeschger events with precipitation increase of ca. 100–200 mm year^?1.     

The atmospheric component of the Mediterranean Sea water budget in a WRF multi-physics ensemble and observations

The use of high resolution atmosphere–ocean coupled regional climate models to study possible future climate changes in the Mediterranean Sea requires an accurate simulation of the atmospheric component of the water budget (i.e., evaporation, precipitation and runoff). A specific configuration of the version 3.1 of the weather research and forecasting (WRF) regional climate model was shown to systematically overestimate the Mediterranean Sea water budget mainly due to an excess of evaporation (~1,450 mm yr^?1) compared with observed estimations (~1,150 mm yr^?1). In this article, a 70-member multi-physics ensemble is used to try to understand the relative importance of various sub-grid scale processes in the Mediterranean Sea water budget and to evaluate its representation by comparing simulated results with observed-based estimates. The physics ensemble was constructed by performing 70 1-year long simulations using version 3.3 of the WRF model by combining six cumulus, four surface/planetary boundary layer and three radiation schemes. Results show that evaporation variability across the multi-physics ensemble (～10 % of the mean evaporation) is dominated by the choice of the surface layer scheme that explains more than ～70 % of the total variance and that the overestimation of evaporation in WRF simulations is generally related with an overestimation of surface exchange coefficients due to too large values of the surface roughness parameter and/or the simulation of too unstable surface conditions. Although the influence of radiation schemes on evaporation variability is small (～13 % of the total variance), radiation schemes strongly influence exchange coefficients and vertical humidity gradients near the surface due to modifications of temperature lapse rates. The precipitation variability across the physics ensemble (～35 % of the mean precipitation) is dominated by the choice of both cumulus (～55 % of the total variance) and planetary boundary layer (～32 % of the total variance) schemes with a strong regional dependence. Most members of the ensemble underestimate total precipitation amounts with biases as large as 250 mm yr^?1 over the whole Mediterranean Sea compared with ERA Interim reanalysis mainly due to an underestimation of the number of wet days. The larger number of dry days in simulations is associated with a deficit in the activation of cumulus schemes. Both radiation and planetary boundary layer schemes influence precipitation through modifications on the available water vapor in the boundary layer generally tied with changes in evaporation.     

COMPARISON OF PERSIANN AND TMPA DAILY PRECIPITATION ESTIMATES OVER HUNAN PROVINCE OF CHINA

This study presented a detailed comparison of daily precipitation estimates from Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) and Tropical Rainfall Measuring Mission (TRMM) Multi -satellite Precipitation Analysis (TMPA) over Hunan province of China from 1998 to 2014. The ground gauge observations are taken as the reference. It is found that overall TMPA clearly outperforms PERSIANN, indicating by better statistical metrics (including correlation coefficient, root mean square error and relative bias). For the geospatial pattern, although both products are able to capture the major precipitation features (e.g., precipitation geospatial homogeneity) in Hunan, yet PERSIANN largely underestimates the precipitation intensity throughout all seasons. In contrast, there is no clear bias tendency from TMPA estimates. Precipitation intensity analysis showed that both the occurrence and amount histograms from TMPA are closer to the gauge observations from spring to autumn. However, in the winter season PERSIANN is closer to gauge observation, which is likely due to the ground contamination from the passive microwave sensors used by TMPA.     

Dynamic downscaling of 22-year CFS winter seasonal hindcasts with the UCLA-ETA regional climate model over the United States

This study evaluates the UCLA-ETA regional model’s dynamic downscaling ability to improve the National Center for Environmental Prediction Climate Forecast System (NCEP CFS), winter season predictions over the contiguous United States (US). Spatial distributions and temporal variations of seasonal and monthly precipitation are the main focus. A multi-member ensemble means of 22 winters from 1982 through 2004 are included in the study. CFS over-predicts the precipitation in eastern and western US by as much as 45 and 90 % on average compared to observations, respectively. Dynamic downscaling improves the precipitation hindcasts across the domain, except in the southern States, by substantially reducing the excessive precipitation produced by the CFS. Average precipitation root-mean-square error for CFS and UCLA-ETA are 1.5 and 0.9 mm day^?1, respectively. In addition, downscaling improves the simulation of spatial distribution of snow water equivalent and land surface heat fluxes. Despite these large improvements, the UCLA-ETA’s ability to improve the inter-annual and intra-seasonal precipitation variability is not clear, probably because of the imposed CFS’ lateral boundary conditions. Preliminary analysis of the cause for the large precipitation differences between the models reveals that the CFS appears to underestimate the moisture flux convergence despite producing excessive precipitation amounts. Additionally, the comparison of modeled monthly surface sensible and latent heat fluxes with Global Land Data Assimilation System land data set shows that the CFS incorrectly partitioned most of surface energy into evaporation, unlike the UCLA-ETA. These findings suggest that the downscaling improvements are mostly due to a better representation of land-surface processes by the UCLA-ETA. Sensitivity tests also reveal that higher-resolution topography only played a secondary role in the dynamic downscaling improvement.     

A regional climate change simulation over East Asia

In this study, regional climate changes for seventy years (1980–2049) over East Asia and the Korean Peninsula are investigated using the Special Reports on Emission Scenarios (SRES) B1 scenario via a high-resolution regional climate model, and the impact of global warming on extreme climate events over the study area is investigated. According to future climate predictions for East Asia, the annual mean surface air temperature increases by 1.8°C and precipitation decreases by 0.2 mm day^?1 (2030–2049). The maximum wind intensity of tropical cyclones increases in the high wind categories, and the intra-seasonal variation of tropical cyclone occurrence changes in the western North Pacific. The predicted increase in surface air temperature results from increased longwave radiations at the surface. The predicted decrease in precipitation is caused primarily by northward shift of the monsoon rain-band due to the intensified subtropical high. In the nested higher-resolution (20 km) simulation over the Korean Peninsula, annual mean surface air temperature increases by 1.5°C and annual mean precipitation decreases by 0.2 mm day^?1. Future surface air temperature over the Korean Peninsula increases in all seasons due to surface temperature warming, which leads to changes in the length of the four seasons. Future total precipitation over the Korean Peninsula is decreased, but the intensity and occurrence of heavy precipitation events increases. The regional climate changes information from this study can be used as a fruitful reference in climate change studies over East Asia and the Korean peninsula.     

How well are daily intense rainfall events captured by current climate models over Africa?

The ability of state-of-the-art climate models to capture the mean spatial and temporal characteristics of daily intense rainfall events over Africa is evaluated by analyzing regional climate model (RCM) simulations at 90- and 30-km along with output from four atmospheric general circulation models (AGCMs) and coupled atmosphere–ocean general circulation models (AOGCMs) of the Climate Model Intercomparison Project 5. Daily intense rainfall events are extracted at grid point scale using a 95th percentile threshold approach applied to all rainy days (i.e., daily rainfall ≥1 mm day^?1) over the 1998–2008 period for which two satellite-derived precipitation products are available. Both RCM simulations provide similar results. They accurately capture the spatial and temporal characteristics of intense events, while they tend to overestimate their number and underestimate their intensity. The skill of AGCMs and AOGCMs is generally similar over the African continent and similar to previous global climate model generations. The majority of the AGCMs and AOGCMs greatly overestimate the frequency of intense events, particularly in the tropics, generally fail at simulating the observed intensity, and systematically overestimate their spatial coverage. The RCM performs at least as well as the most accurate global climate model, demonstrating a clear added value to general circulation model simulations and the usefulness of regional modeling for investigating the physics leading to intense events and their change under global warming.     

Seasonal variation of lightning activity over the Indian subcontinent

The seasonal variation of lightning flash activity over the Indian subcontinent (0°N–35°N and 60°E–100°E) is studied using the quality checked monthly lightning flash data obtained from lightning imaging sensor on board the Tropical Rainfall Measuring Mission satellite. This paper presents results of spatio-temporal variability of lightning activity over the Indian subcontinent. The study of seasonal total lightning flashes indicates that the lightning flash density values are in qualitative agreement with the convective activity observed over this region. Maximum seasonal total flash counts are observed during the monsoon season. The propagation of the inter-tropical convergence zone over this region is also confirmed. Synoptic conditions responsible for variation of lightning activity are also investigated with the help of an observed dataset. The mean monthly flash counts show a peak in the month of May, which is the month of maximum temperatures over this region. Maximum flash density (40.2 km^?2 season^?1) is observed during the pre-monsoon season at 25.2°N/91.6°E and the annual maximum flash density of 28.2 km^?2 year^?1 is observed at 33.2°N/74.6°E. The study of the inter-annual variability of flash counts exhibits bimodal nature with the first maximum in April/May and second maximum in August/September.     

Validation of high-resolution TRMM-3B43 precipitation product using rain gauge measurements over Kyrgyzstan

This paper presents the validation of monthly precipitation using Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA)—3B43 product with conventional rain gauge observations for the period 1998–2007 over Kyrgyzstan. This study is carried out to quantify the accuracy of TMPA-3B43 product over the high latitude and complex orographic region. The present work is quite important because it is highly desirable to compare the TMPA precipitation product with the ground truth data at a regional scale, so that the satellite product can be fine-tuned at that scale. For the validation, four different types of spatial collocation have been used: station wise, climatic zone wise, topographically and seasonal. The analysis has been done at the same spatial and temporal scales in order to eliminate the sampling biases in the comparisons. The results show that TMPA-3B43 product has statistically significant correlation (r?=?0.36–0.88) with rain gauge data over the most parts of the country. The minimum linear correlation is observed around the large continental water bodies (e.g., Issyk-Kul lake; r?=?0.17–0.19). The overall result suggests that the precipitation estimated using TMPA-3B43 product performs reasonably well over the plain regions and even over the orographic regions except near the big lake regions. Also, the negative bias suggests the systematic underestimation of high precipitation by TMPA-3B43 product. The analyses suggest the need of a better algorithm for precipitation estimation over this region separately to capture the different types of rain events more reliably.     

Evaluation of TMPA satellite-based research and real-time rainfall estimates during six tropical-related heavy rainfall events over Louisiana, USA

This study focuses on the evaluation of 3-hourly 0.25° × 0.25° satellite-based rainfall estimates produced by the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA). The evaluation is performed during six heavy rainfall events that were generated by tropical storms passing over Louisiana, United States. Two surface-based rainfall datasets from gauge and radar observations are used as a ground reference for evaluating the real-time (RT) version of the TMPA product and the post-real-time bias adjusted research version. The evaluation analysis is performed at the native temporal and spatial scales of the TMPA products, 3-hourly and 0.25° × 0.25°. Several graphical and statistical techniques are applied to characterize the deviation of the TMPA estimates from the reference datasets. Both versions of the TMPA products track reasonably well the temporal evolution and fluctuations of surface rainfall during the analyzed storms with moderate to high correlation values of 0.5–0.8. The TMPA estimates reported reasonable levels of rainfall detection especially when light rainfall rates are excluded. On a storm scale, the TMPA products are characterized by varying degrees of bias which was mostly within ± 25% and ± 50% for the research and RT products, respectively. Analysis of the error distribution indicated that, on average, the TMPA products tend to overestimate small rain rates and underestimate large rain rates. Compared to the real-time estimates, the research product shows significant improvement in the overall and conditional bias, and in the correlation coefficients, with slight deterioration in the probability of detecting rainfall occurrences. A fair agreement in terms of reproducing the tail of the distribution of rain rates (i.e., probability of surface rainfall exceeding certain thresholds) was observed especially for the RT estimates. Despite the apparent differences with surface rainfall estimates, the results reported in this study highlight the TMPA potential as a valuable resource of high-resolution rainfall information over many areas in the world that lack capabilities for monitoring landfalling tropical storms.     

Estimating climate change effects on net primary production of rangelands in the United States

The potential effects of climate change on net primary productivity (NPP) of U.S. rangelands were evaluated using estimated climate regimes from the A1B, A2 and B2 global change scenarios imposed on the biogeochemical cycling model, Biome-BGC from 2001 to 2100. Temperature, precipitation, vapor pressure deficit, day length, solar radiation, CO₂ enrichment and nitrogen deposition were evaluated as drivers of NPP. Across all three scenarios, rangeland NPP increased by 0.26 % year^?1 (7 kg C ha^?1 year^?1) but increases were not apparent until after 2030 and significant regional variation in NPP was revealed. The Desert Southwest and Southwest assessment regions exhibited declines in NPP of about 7 % by 2100, while the Northern and Southern Great Plains, Interior West and Eastern Prairies all experienced increases over 25 %. Grasslands dominated by warm season (C4 photosynthetic pathway) species showed the greatest response to temperature while cool season (C3 photosynthetic pathway) dominated regions responded most strongly to CO₂ enrichment. Modeled NPP responses compared favorably with experimental results from CO₂ manipulation experiments and to NPP estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS). Collectively, these results indicate significant and asymmetric changes in NPP for U.S. rangelands may be expected.